The subject matter disclosed herein relates to gas turbine engines, and more particularly to a system and method for flexible operation of a turbine engine using a model-free adaptive framework.
As gas turbine engines operate, various factors influence the wear and thereby the overall life of the engine components. For example, starting cycles, power settings, fuels, and/or levels of steam or water injection directly influence the life of key gas turbine parts and, thus, may be significant factors in determining maintenance intervals. Additionally, gas turbines wear in different ways depending on, for example, their service conditions and, therefore, will have different wear rates depending on the nature of the services. For example, machines put into peak or cyclic duty may be exposed to greater thermal mechanical fatigue, while machines placed into continuous duty or baseline operation may typically experience greater rupture, creep deflection, oxidation, and/or corrosion. Accordingly, operators of gas turbine engines, faced with various operating conditions and scenarios, must often make decisions that prioritize either life of the turbine engine or performance.
Currently, operator decisions are typically based on computation of maintenance factors performed offline (e.g., separate from the operation of the machinery to be monitored) and may account for factors such as fired hours and/or start hours. However, these calculations often neglect to consider accumulated hours of particular ramp rate, or time spent at part load, as well as other complexities of turbine operation. Further, operators tasked with making life calculations may use thermal models to compute metal temperatures that may take several hours to converge, therefore diminishing the utility of such models for real-time application. Additional drawbacks of existing operational decision and control techniques include the lack of consideration of plant dynamics and the use multiple non-integrated models. There is therefore a need for improved operational control techniques that take into account the complexities of turbine operation, plant dynamics, and integrate multiple thermal models to estimate life versus performance in real-time. Accordingly, the present methods and systems utilize historical data, real-time data, current thermal models, and model-free adaptive techniques to provide systems and methods of more flexible control of gas turbine operation. The resulting techniques may allow for reduced and/or minimized costs as well as increased and/or maximized revenues from the operation of turbine engines.